Robust variable selection for mixture linear regression models

Year 2016, Volume: 45 Issue: 2, 549 - 559, 01.04.2016

Yunlu Jiang

Abstract

In this paper, we propose a robust variable selection to estimate and select relevant covariates for the finite mixture of linear regression models
by assuming that the error terms follow a Laplace distribution to the
data after trimming the high leverage points. We introduce a revised
Expectation-maximization (EM) algorithm for numerical computation.
Simulation studies indicate that the proposed method is robust to both
the high leverage points and outliers in the y-direction, and can obtain
a consistent variable selection in the case of outliers or heavy-tail error
distribution. Finally, we apply the proposed methodology to analyze a
real data.

Keywords

Finite mixture of linear regression models, Robustness, EM-algorithm

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